Author: Jean-Paul Fox
Publisher:
ISBN:
Category :
Languages : en
Pages : 68

Get Book Here

Book Description

Author: Jean-Paul Fox
Publisher: Springer Science & Business Media
ISBN: 1441907424
Category : Social Science
Languages : en
Pages : 323

Get Book Here

Book Description
The modeling of item response data is governed by item response theory, also referred to as modern test theory. The eld of inquiry of item response theory has become very large and shows the enormous progress that has been made. The mainstream literature is focused on frequentist statistical methods for - timating model parameters and evaluating model t. However, the Bayesian methodology has shown great potential, particularly for making further - provements in the statistical modeling process. The Bayesian approach has two important features that make it attractive for modeling item response data. First, it enables the possibility of incorpor- ing nondata information beyond the observed responses into the analysis. The Bayesian methodology is also very clear about how additional information can be used. Second, the Bayesian approach comes with powerful simulation-based estimation methods. These methods make it possible to handle all kinds of priors and data-generating models. One of my motives for writing this book is to give an introduction to the Bayesian methodology for modeling and analyzing item response data. A Bayesian counterpart is presented to the many popular item response theory books (e.g., Baker and Kim 2004; De Boeck and Wilson, 2004; Hambleton and Swaminathan, 1985; van der Linden and Hambleton, 1997) that are mainly or completely focused on frequentist methods. The usefulness of the Bayesian methodology is illustrated by discussing and applying a range of Bayesian item response models.

Author: Wim J. van der Linden
Publisher: CRC Press
ISBN: 1351645455
Category : Mathematics
Languages : en
Pages : 1584

Get Book Here

Book Description
Drawing on the work of 75 internationally acclaimed experts in the field, Handbook of Item Response Theory, Three-Volume Set presents all major item response models, classical and modern statistical tools used in item response theory (IRT), and major areas of applications of IRT in educational and psychological testing, medical diagnosis of patient-reported outcomes, and marketing research. It also covers CRAN packages, WinBUGS, Bilog MG, Multilog, Parscale, IRTPRO, Mplus, GLLAMM, Latent Gold, and numerous other software tools. A full update of editor Wim J. van der Linden and Ronald K. Hambleton’s classic Handbook of Modern Item Response Theory, this handbook has been expanded from 28 chapters to 85 chapters in three volumes. The three volumes are thoroughly edited and cross-referenced, with uniform notation, format, and pedagogical principles across all chapters. Each chapter is self-contained and deals with the latest developments in IRT.

Author: Roy Levy
Publisher: CRC Press
ISBN: 1439884684
Category : Mathematics
Languages : en
Pages : 480

Get Book Here

Book Description
A Single Cohesive Framework of Tools and Procedures for Psychometrics and Assessment Bayesian Psychometric Modeling presents a unified Bayesian approach across traditionally separate families of psychometric models. It shows that Bayesian techniques, as alternatives to conventional approaches, offer distinct and profound advantages in achieving many goals of psychometrics. Adopting a Bayesian approach can aid in unifying seemingly disparate—and sometimes conflicting—ideas and activities in psychometrics. This book explains both how to perform psychometrics using Bayesian methods and why many of the activities in psychometrics align with Bayesian thinking. The first part of the book introduces foundational principles and statistical models, including conceptual issues, normal distribution models, Markov chain Monte Carlo estimation, and regression. Focusing more directly on psychometrics, the second part covers popular psychometric models, including classical test theory, factor analysis, item response theory, latent class analysis, and Bayesian networks. Throughout the book, procedures are illustrated using examples primarily from educational assessments. A supplementary website provides the datasets, WinBUGS code, R code, and Netica files used in the examples.

Author: Wim J. van der Linden
Publisher: CRC Press
ISBN: 1466514434
Category : Mathematics
Languages : en
Pages : 442

Get Book Here

Book Description
Drawing on the work of internationally acclaimed experts in the field, Handbook of Item Response Theory, Volume Two: Statistical Tools presents classical and modern statistical tools used in item response theory (IRT). While IRT heavily depends on the use of statistical tools for handling its models and applications, systematic introductions and reviews that emphasize their relevance to IRT are hardly found in the statistical literature. This second volume in a three-volume set fills this void. Volume Two covers common probability distributions, the issue of models with both intentional and nuisance parameters, the use of information criteria, methods for dealing with missing data, and model identification issues. It also addresses recent developments in parameter estimation and model fit and comparison, such as Bayesian approaches, specifically Markov chain Monte Carlo (MCMC) methods.

Author: Christine DeMars
Publisher: Oxford University Press
ISBN: 0195377036
Category : Medical
Languages : en
Pages : 138

Get Book Here

Book Description
This volume guides its reader through the basics of Item Response Theory, with an emphasis on what and how to include relevant information in the methods and results sections of professional papers. The author offers examples of good and bad write-ups.

Author: Paul de Boeck
Publisher: Springer Science & Business Media
ISBN: 1475739907
Category : Social Science
Languages : en
Pages : 394

Get Book Here

Book Description
This edited volume gives a new and integrated introduction to item response models (predominantly used in measurement applications in psychology, education, and other social science areas) from the viewpoint of the statistical theory of generalized linear and nonlinear mixed models. It also includes a chapter on the statistical background and one on useful software.

Author: Peter Congdon
Publisher: John Wiley & Sons
ISBN: 0470035935
Category : Mathematics
Languages : en
Pages : 596

Get Book Here

Book Description
Bayesian methods combine the evidence from the data at hand with previous quantitative knowledge to analyse practical problems in a wide range of areas. The calculations were previously complex, but it is now possible to routinely apply Bayesian methods due to advances in computing technology and the use of new sampling methods for estimating parameters. Such developments together with the availability of freeware such as WINBUGS and R have facilitated a rapid growth in the use of Bayesian methods, allowing their application in many scientific disciplines, including applied statistics, public health research, medical science, the social sciences and economics. Following the success of the first edition, this reworked and updated book provides an accessible approach to Bayesian computing and analysis, with an emphasis on the principles of prior selection, identification and the interpretation of real data sets. The second edition: Provides an integrated presentation of theory, examples, applications and computer algorithms. Discusses the role of Markov Chain Monte Carlo methods in computing and estimation. Includes a wide range of interdisciplinary applications, and a large selection of worked examples from the health and social sciences. Features a comprehensive range of methodologies and modelling techniques, and examines model fitting in practice using Bayesian principles. Provides exercises designed to help reinforce the reader’s knowledge and a supplementary website containing data sets and relevant programs. Bayesian Statistical Modelling is ideal for researchers in applied statistics, medical science, public health and the social sciences, who will benefit greatly from the examples and applications featured. The book will also appeal to graduate students of applied statistics, data analysis and Bayesian methods, and will provide a great source of reference for both researchers and students. Praise for the First Edition: “It is a remarkable achievement to have carried out such a range of analysis on such a range of data sets. I found this book comprehensive and stimulating, and was thoroughly impressed with both the depth and the range of the discussions it contains.” – ISI - Short Book Reviews “This is an excellent introductory book on Bayesian modelling techniques and data analysis” – Biometrics “The book fills an important niche in the statistical literature and should be a very valuable resource for students and professionals who are utilizing Bayesian methods.” – Journal of Mathematical Psychology

Author: Wim J. van der Linden
Publisher: CRC Press
ISBN: 1315360446
Category : Mathematics
Languages : en
Pages : 493

Get Book Here

Book Description
Drawing on the work of internationally acclaimed experts in the field, Handbook of Item Response Theory, Volume Two: Statistical Tools presents classical and modern statistical tools used in item response theory (IRT). While IRT heavily depends on the use of statistical tools for handling its models and applications, systematic introductions and reviews that emphasize their relevance to IRT are hardly found in the statistical literature. This second volume in a three-volume set fills this void. Volume Two covers common probability distributions, the issue of models with both intentional and nuisance parameters, the use of information criteria, methods for dealing with missing data, and model identification issues. It also addresses recent developments in parameter estimation and model fit and comparison, such as Bayesian approaches, specifically Markov chain Monte Carlo (MCMC) methods.

Author: Jennifer Lee Weeding
Publisher:
ISBN:
Category : Errors-in-variables models
Languages : en
Pages : 155

Get Book Here

Book Description
Measurement errors arise in a variety of circumstances and occur when a variable cannot be observed exactly, but instead is observed with error. For example, summary measures contain measurement error, as the true value of the variable is estimated from observed data that contain sampling variability. Measurement errors should be accounted for when they are present, as the impacts of ignoring measurement errors include bias in parameter estimates and a loss of power to detect effects. Measurement error models are used to account for measurement errors and correct parameter estimates for the bias induced from variables measured with error. To account for measurement errors when present, most correction methods require that the measurement error variance be known (or estimated). Common correction methods include the method of moments correction, the SIMEX correction, and Bayesian correction methods. The area under the curve (AUC) summary measure is commonly used in pharmaceutical studies to estimate the total concentration of a substance present in the blood over a given time interval. Other areas, such as Ecology, use the AUC to estimate the total count of a species present over a specified time interval. In situations where the AUC is estimated, a measure of the uncertainty associated with it is often desired. Due to the longitudinal nature of AUC data, the estimation of its variance is often not straightforward. In this research we develop a Bayesian method to estimate the variance of the AUC, where our focus is on accounting for the possible correlation structure between repeated observations on the same subject. This estimate can then be used in measurement error models to account for the measurement error induced from estimating the AUC. We study the performance of three measurement error correction methods in the simple linear regression setting, where measurement errors are present in the explanatory variable, the response variable, or both. We extend the Bayesian correction methods to account for uncorrelated and correlated measurement errors between variables. The methods were validated using both simulated and real data collected from an equine study of blood glucose measurements.